With the improvement of high-throughput technology, the dramatic increase of large-scale data in both biomolecular concentration and biomolecular interactions has resulted in many biological networks, such as protein interaction networks, gene regulatory networks, and metabolic networks. Although functional analysis is the fundamental step of better understanding biological networks, utilizing vast wealth of data and huge amount of knowledge to annotate and analyze the function of biological networks is still challenging in nowadays bioinformatics. Many software tools are available to visualize and analyze function-derived biological networks, but most of them are isolated with simple functions. One challenge faced by these visualization tools is how to make sense of such networks often represented as massive “hairballs.” Many network analysis algorithms filter or partition networks based on topological features, or mathematically model networks rely on their statistical properties, sidestepping the issue of making sense of the network itself altogether. On other hand, traditional functional enrichment analysis methods regard a network as a list of genes, and annotate networks with gene set enrichment methods. However, it does not consider the topological dynamics of network which might lead to the different functions under different conditions. Therefore, it is necessary to consider molecular interactions to correctly and specifically annotate biological networks.
As one of the most successful open source frameworks in bioinformatics, Cytoscape is a powerful network visualization platform that actively supports independent plugin development. By integrating model-view-controller design pattern and Cytoscape techniques, it makes possible an integrated ontology-annotated biological network visualization and analysis platform. In the first stage of the project, we successfully developed two interactive plugins -- Mosaic (http://nrnb.org/tools/mosaic) and NOA (http://nrnb.org/tools/noa) -- to address both visualization and analysis respectively. Mosaic supports interactive network annotation and visualization that includes partitioning, layout and coloring based on biologically-relevant ontologies. It shows slices of a given network in the visual language of biological pathways, which are familiar to any biologist and are ideal frameworks for integrating knowledge, and also provides researchers with an interactive tool to evaluate biological interactions within the context of well-defined processes, functions and cellular localization while retaining all original network information. NOA first introduced link ontology that assigns functions to interactions based on the known annotations of joint genes via optimizing two novel indexes ‘Coverage’ and ‘Diversity’. Then, NOA generates two alternative reference sets to statistically rank the enriched functional terms for a given biological network. It has been proved to be more efficient not only in...
4. Importance and common drawbacks
• Function enrichment analysis
– DAVID (2003) Genome biol 3,364 (3,293)
– DAVID (2008) Nature protcols 1,908 (1,802)
– Bingo (2005) Bioinformatics 657 (609)
• Limitations
– Lack of good visualization tool
– All for gene set, not network
4
Background
5. Workflow
Visualization
+
Analysis
•Including as more as
possible functions of other
software
•Relatively independent
modules with interactions
•Very flexible, 4 individual
plugins or 1 bundle
5
Background
7. Visualization (Mosaic)
• Mosaic shows slices of a given network in the visual
language of biological pathways, which are familiar
to any biologist and are ideal frameworks for
integrating knowledge
Biological
Process
Molecular
Cellular function
components
7
Mosaic
8. Visualization (Mosaic)
• Partitioning with BP
• Layout with CC
• Coloring with MF
• Automatic annotation
• ID mapping
• Overview network
.
8
Mosaic
